< Return to Video

A monkey economy as irrational as ours

  • 0:02 - 0:04
    I want to start my talk today with two observations
  • 0:04 - 0:06
    about the human species.
  • 0:06 - 0:09
    The first observation is something that you might think is quite obvious,
  • 0:09 - 0:11
    and that's that our species, Homo sapiens,
  • 0:11 - 0:13
    is actually really, really smart --
  • 0:13 - 0:15
    like, ridiculously smart --
  • 0:15 - 0:17
    like you're all doing things
  • 0:17 - 0:20
    that no other species on the planet does right now.
  • 0:20 - 0:22
    And this is, of course,
  • 0:22 - 0:24
    not the first time you've probably recognized this.
  • 0:24 - 0:27
    Of course, in addition to being smart, we're also an extremely vain species.
  • 0:27 - 0:30
    So we like pointing out the fact that we're smart.
  • 0:30 - 0:32
    You know, so I could turn to pretty much any sage
  • 0:32 - 0:34
    from Shakespeare to Stephen Colbert
  • 0:34 - 0:36
    to point out things like the fact that
  • 0:36 - 0:38
    we're noble in reason and infinite in faculties
  • 0:38 - 0:40
    and just kind of awesome-er than anything else on the planet
  • 0:40 - 0:43
    when it comes to all things cerebral.
  • 0:43 - 0:45
    But of course, there's a second observation about the human species
  • 0:45 - 0:47
    that I want to focus on a little bit more,
  • 0:47 - 0:49
    and that's the fact that
  • 0:49 - 0:52
    even though we're actually really smart, sometimes uniquely smart,
  • 0:52 - 0:55
    we can also be incredibly, incredibly dumb
  • 0:55 - 0:58
    when it comes to some aspects of our decision making.
  • 0:58 - 1:00
    Now I'm seeing lots of smirks out there.
  • 1:00 - 1:02
    Don't worry, I'm not going to call anyone in particular out
  • 1:02 - 1:04
    on any aspects of your own mistakes.
  • 1:04 - 1:06
    But of course, just in the last two years
  • 1:06 - 1:09
    we see these unprecedented examples of human ineptitude.
  • 1:09 - 1:12
    And we've watched as the tools we uniquely make
  • 1:12 - 1:14
    to pull the resources out of our environment
  • 1:14 - 1:16
    kind of just blow up in our face.
  • 1:16 - 1:18
    We've watched the financial markets that we uniquely create --
  • 1:18 - 1:21
    these markets that were supposed to be foolproof --
  • 1:21 - 1:23
    we've watched them kind of collapse before our eyes.
  • 1:23 - 1:25
    But both of these two embarrassing examples, I think,
  • 1:25 - 1:28
    don't highlight what I think is most embarrassing
  • 1:28 - 1:30
    about the mistakes that humans make,
  • 1:30 - 1:33
    which is that we'd like to think that the mistakes we make
  • 1:33 - 1:35
    are really just the result of a couple bad apples
  • 1:35 - 1:38
    or a couple really sort of FAIL Blog-worthy decisions.
  • 1:38 - 1:41
    But it turns out, what social scientists are actually learning
  • 1:41 - 1:44
    is that most of us, when put in certain contexts,
  • 1:44 - 1:47
    will actually make very specific mistakes.
  • 1:47 - 1:49
    The errors we make are actually predictable.
  • 1:49 - 1:51
    We make them again and again.
  • 1:51 - 1:53
    And they're actually immune to lots of evidence.
  • 1:53 - 1:55
    When we get negative feedback,
  • 1:55 - 1:58
    we still, the next time we're face with a certain context,
  • 1:58 - 2:00
    tend to make the same errors.
  • 2:00 - 2:02
    And so this has been a real puzzle to me
  • 2:02 - 2:04
    as a sort of scholar of human nature.
  • 2:04 - 2:06
    What I'm most curious about is,
  • 2:06 - 2:09
    how is a species that's as smart as we are
  • 2:09 - 2:11
    capable of such bad
  • 2:11 - 2:13
    and such consistent errors all the time?
  • 2:13 - 2:16
    You know, we're the smartest thing out there, why can't we figure this out?
  • 2:16 - 2:19
    In some sense, where do our mistakes really come from?
  • 2:19 - 2:22
    And having thought about this a little bit, I see a couple different possibilities.
  • 2:22 - 2:25
    One possibility is, in some sense, it's not really our fault.
  • 2:25 - 2:27
    Because we're a smart species,
  • 2:27 - 2:29
    we can actually create all kinds of environments
  • 2:29 - 2:31
    that are super, super complicated,
  • 2:31 - 2:34
    sometimes too complicated for us to even actually understand,
  • 2:34 - 2:36
    even though we've actually created them.
  • 2:36 - 2:38
    We create financial markets that are super complex.
  • 2:38 - 2:41
    We create mortgage terms that we can't actually deal with.
  • 2:41 - 2:44
    And of course, if we are put in environments where we can't deal with it,
  • 2:44 - 2:46
    in some sense makes sense that we actually
  • 2:46 - 2:48
    might mess certain things up.
  • 2:48 - 2:50
    If this was the case, we'd have a really easy solution
  • 2:50 - 2:52
    to the problem of human error.
  • 2:52 - 2:54
    We'd actually just say, okay, let's figure out
  • 2:54 - 2:56
    the kinds of technologies we can't deal with,
  • 2:56 - 2:58
    the kinds of environments that are bad --
  • 2:58 - 3:00
    get rid of those, design things better,
  • 3:00 - 3:02
    and we should be the noble species
  • 3:02 - 3:04
    that we expect ourselves to be.
  • 3:04 - 3:07
    But there's another possibility that I find a little bit more worrying,
  • 3:07 - 3:10
    which is, maybe it's not our environments that are messed up.
  • 3:10 - 3:13
    Maybe it's actually us that's designed badly.
  • 3:13 - 3:15
    This is a hint that I've gotten
  • 3:15 - 3:18
    from watching the ways that social scientists have learned about human errors.
  • 3:18 - 3:21
    And what we see is that people tend to keep making errors
  • 3:21 - 3:24
    exactly the same way, over and over again.
  • 3:24 - 3:26
    It feels like we might almost just be built
  • 3:26 - 3:28
    to make errors in certain ways.
  • 3:28 - 3:31
    This is a possibility that I worry a little bit more about,
  • 3:31 - 3:33
    because, if it's us that's messed up,
  • 3:33 - 3:35
    it's not actually clear how we go about dealing with it.
  • 3:35 - 3:38
    We might just have to accept the fact that we're error prone
  • 3:38 - 3:40
    and try to design things around it.
  • 3:40 - 3:43
    So this is the question my students and I wanted to get at.
  • 3:43 - 3:46
    How can we tell the difference between possibility one and possibility two?
  • 3:46 - 3:48
    What we need is a population
  • 3:48 - 3:50
    that's basically smart, can make lots of decisions,
  • 3:50 - 3:52
    but doesn't have access to any of the systems we have,
  • 3:52 - 3:54
    any of the things that might mess us up --
  • 3:54 - 3:56
    no human technology, human culture,
  • 3:56 - 3:58
    maybe even not human language.
  • 3:58 - 4:00
    And so this is why we turned to these guys here.
  • 4:00 - 4:03
    These are one of the guys I work with. This is a brown capuchin monkey.
  • 4:03 - 4:05
    These guys are New World primates,
  • 4:05 - 4:07
    which means they broke off from the human branch
  • 4:07 - 4:09
    about 35 million years ago.
  • 4:09 - 4:11
    This means that your great, great, great great, great, great --
  • 4:11 - 4:13
    with about five million "greats" in there --
  • 4:13 - 4:15
    grandmother was probably the same great, great, great, great
  • 4:15 - 4:17
    grandmother with five million "greats" in there
  • 4:17 - 4:19
    as Holly up here.
  • 4:19 - 4:22
    You know, so you can take comfort in the fact that this guy up here is a really really distant,
  • 4:22 - 4:24
    but albeit evolutionary, relative.
  • 4:24 - 4:26
    The good news about Holly though is that
  • 4:26 - 4:29
    she doesn't actually have the same kinds of technologies we do.
  • 4:29 - 4:32
    You know, she's a smart, very cut creature, a primate as well,
  • 4:32 - 4:34
    but she lacks all the stuff we think might be messing us up.
  • 4:34 - 4:36
    So she's the perfect test case.
  • 4:36 - 4:39
    What if we put Holly into the same context as humans?
  • 4:39 - 4:41
    Does she make the same mistakes as us?
  • 4:41 - 4:43
    Does she not learn from them? And so on.
  • 4:43 - 4:45
    And so this is the kind of thing we decided to do.
  • 4:45 - 4:47
    My students and I got very excited about this a few years ago.
  • 4:47 - 4:49
    We said, all right, let's, you know, throw so problems at Holly,
  • 4:49 - 4:51
    see if she messes these things up.
  • 4:51 - 4:54
    First problem is just, well, where should we start?
  • 4:54 - 4:56
    Because, you know, it's great for us, but bad for humans.
  • 4:56 - 4:58
    We make a lot of mistakes in a lot of different contexts.
  • 4:58 - 5:00
    You know, where are we actually going to start with this?
  • 5:00 - 5:03
    And because we started this work around the time of the financial collapse,
  • 5:03 - 5:05
    around the time when foreclosures were hitting the news,
  • 5:05 - 5:07
    we said, hhmm, maybe we should
  • 5:07 - 5:09
    actually start in the financial domain.
  • 5:09 - 5:12
    Maybe we should look at monkey's economic decisions
  • 5:12 - 5:15
    and try to see if they do the same kinds of dumb things that we do.
  • 5:15 - 5:17
    Of course, that's when we hit a sort second problem --
  • 5:17 - 5:19
    a little bit more methodological --
  • 5:19 - 5:21
    which is that, maybe you guys don't know,
  • 5:21 - 5:24
    but monkeys don't actually use money. I know, you haven't met them.
  • 5:24 - 5:26
    But this is why, you know, they're not in the queue behind you
  • 5:26 - 5:29
    at the grocery store or the ATM -- you know, they don't do this stuff.
  • 5:29 - 5:32
    So now we faced, you know, a little bit of a problem here.
  • 5:32 - 5:34
    How are we actually going to ask monkeys about money
  • 5:34 - 5:36
    if they don't actually use it?
  • 5:36 - 5:38
    So we said, well, maybe we should just, actually just suck it up
  • 5:38 - 5:40
    and teach monkeys how to use money.
  • 5:40 - 5:42
    So that's just what we did.
  • 5:42 - 5:45
    What you're looking at over here is actually the first unit that I know of
  • 5:45 - 5:47
    of non-human currency.
  • 5:47 - 5:49
    We weren't very creative at the time we started these studies,
  • 5:49 - 5:51
    so we just called it a token.
  • 5:51 - 5:54
    But this is the unit of currency that we've taught our monkeys at Yale
  • 5:54 - 5:56
    to actually use with humans,
  • 5:56 - 5:59
    to actually buy different pieces of food.
  • 5:59 - 6:01
    It doesn't look like much -- in fact, it isn't like much.
  • 6:01 - 6:03
    Like most of our money, it's just a piece of metal.
  • 6:03 - 6:06
    As those of you who've taken currencies home from your trip know,
  • 6:06 - 6:08
    once you get home, it's actually pretty useless.
  • 6:08 - 6:10
    It was useless to the monkeys at first
  • 6:10 - 6:12
    before they realized what they could do with it.
  • 6:12 - 6:14
    When we first gave it to them in their enclosures,
  • 6:14 - 6:16
    they actually kind of picked them up, looked at them.
  • 6:16 - 6:18
    They were these kind of weird things.
  • 6:18 - 6:20
    But very quickly, the monkeys realized
  • 6:20 - 6:22
    that they could actually hand these tokens over
  • 6:22 - 6:25
    to different humans in the lab for some food.
  • 6:25 - 6:27
    And so you see one of our monkeys, Mayday, up here doing this.
  • 6:27 - 6:30
    This is A and B are kind of the points where she's sort of a little bit
  • 6:30 - 6:32
    curious about these things -- doesn't know.
  • 6:32 - 6:34
    There's this waiting hand from a human experimenter,
  • 6:34 - 6:37
    and Mayday quickly figures out, apparently the human wants this.
  • 6:37 - 6:39
    Hands it over, and then gets some food.
  • 6:39 - 6:41
    It turns out not just Mayday, all of our monkeys get good
  • 6:41 - 6:43
    at trading tokens with human salesman.
  • 6:43 - 6:45
    So here's just a quick video of what this looks like.
  • 6:45 - 6:48
    Here's Mayday. She's going to be trading a token for some food
  • 6:48 - 6:51
    and waiting happily and getting her food.
  • 6:51 - 6:53
    Here's Felix, I think. He's our alpha male; he's a kind of big guy.
  • 6:53 - 6:56
    But he too waits patiently, gets his food and goes on.
  • 6:56 - 6:58
    So the monkeys get really good at this.
  • 6:58 - 7:01
    They're surprisingly good at this with very little training.
  • 7:01 - 7:03
    We just allowed them to pick this up on their own.
  • 7:03 - 7:05
    The question is: is this anything like human money?
  • 7:05 - 7:07
    Is this a market at all,
  • 7:07 - 7:09
    or did we just do a weird psychologist's trick
  • 7:09 - 7:11
    by getting monkeys to do something,
  • 7:11 - 7:13
    looking smart, but not really being smart.
  • 7:13 - 7:16
    And so we said, well, what would the monkeys spontaneously do
  • 7:16 - 7:19
    if this was really their currency, if they were really using it like money?
  • 7:19 - 7:21
    Well, you might actually imagine them
  • 7:21 - 7:23
    to do all the kinds of smart things
  • 7:23 - 7:26
    that humans do when they start exchanging money with each other.
  • 7:26 - 7:29
    You might have them start paying attention to price,
  • 7:29 - 7:31
    paying attention to how much they buy --
  • 7:31 - 7:34
    sort of keeping track of their monkey token, as it were.
  • 7:34 - 7:36
    Do the monkeys do anything like this?
  • 7:36 - 7:39
    And so our monkey marketplace was born.
  • 7:39 - 7:41
    The way this works is that
  • 7:41 - 7:44
    our monkeys normally live in a kind of big zoo social enclosure.
  • 7:44 - 7:46
    When they get a hankering for some treats,
  • 7:46 - 7:48
    we actually allowed them a way out
  • 7:48 - 7:50
    into a little smaller enclosure where they could enter the market.
  • 7:50 - 7:52
    Upon entering the market --
  • 7:52 - 7:54
    it was actually a much more fun market for the monkeys than most human markets
  • 7:54 - 7:57
    because, as the monkeys entered the door of the market,
  • 7:57 - 7:59
    a human would give them a big wallet full of tokens
  • 7:59 - 8:01
    so they could actually trade the tokens
  • 8:01 - 8:03
    with one of these two guys here --
  • 8:03 - 8:05
    two different possible human salesmen
  • 8:05 - 8:07
    that they could actually buy stuff from.
  • 8:07 - 8:09
    The salesmen were students from my lab.
  • 8:09 - 8:11
    They dressed differently; they were different people.
  • 8:11 - 8:14
    And over time, they did basically the same thing
  • 8:14 - 8:16
    so the monkeys could learn, you know,
  • 8:16 - 8:19
    who sold what at what price -- you know, who was reliable, who wasn't, and so on.
  • 8:19 - 8:21
    And you can see that each of the experimenters
  • 8:21 - 8:24
    is actually holding up a little, yellow food dish.
  • 8:24 - 8:26
    and that's what the monkey can for a single token.
  • 8:26 - 8:28
    So everything costs one token,
  • 8:28 - 8:30
    but as you can see, sometimes tokens buy more than others,
  • 8:30 - 8:32
    sometimes more grapes than others.
  • 8:32 - 8:35
    So I'll show you a quick video of what this marketplace actually looks like.
  • 8:35 - 8:38
    Here's a monkey-eye-view. Monkeys are shorter, so it's a little short.
  • 8:38 - 8:40
    But here's Honey.
  • 8:40 - 8:42
    She's waiting for the market to open a little impatiently.
  • 8:42 - 8:45
    All of a sudden the market opens. Here's her choice: one grapes or two grapes.
  • 8:45 - 8:47
    You can see Honey, very good market economist,
  • 8:47 - 8:50
    goes with the guy who gives more.
  • 8:50 - 8:52
    She could teach our financial advisers a few things or two.
  • 8:52 - 8:54
    So not just Honey,
  • 8:54 - 8:57
    most of the monkeys went with guys who had more.
  • 8:57 - 8:59
    Most of the monkeys went with guys who had better food.
  • 8:59 - 9:02
    When we introduced sales, we saw the monkeys paid attention to that.
  • 9:02 - 9:05
    They really cared about their monkey token dollar.
  • 9:05 - 9:08
    The more surprising thing was that when we collaborated with economists
  • 9:08 - 9:11
    to actually look at the monkeys' data using economic tools,
  • 9:11 - 9:14
    they basically matched, not just qualitatively,
  • 9:14 - 9:16
    but quantitatively with what we saw
  • 9:16 - 9:18
    humans doing in a real market.
  • 9:18 - 9:20
    So much so that, if you saw the monkeys' numbers,
  • 9:20 - 9:23
    you couldn't tell whether they came from a monkey or a human in the same market.
  • 9:23 - 9:25
    And what we'd really thought we'd done
  • 9:25 - 9:27
    is like we'd actually introduced something
  • 9:27 - 9:29
    that, at least for the monkeys and us,
  • 9:29 - 9:31
    works like a real financial currency.
  • 9:31 - 9:34
    Question is: do the monkeys start messing up in the same ways we do?
  • 9:34 - 9:37
    Well, we already saw anecdotally a couple of signs that they might.
  • 9:37 - 9:39
    One thing we never saw in the monkey marketplace
  • 9:39 - 9:41
    was any evidence of saving --
  • 9:41 - 9:43
    you know, just like our own species.
  • 9:43 - 9:45
    The monkeys entered the market, spent their entire budget
  • 9:45 - 9:47
    and then went back to everyone else.
  • 9:47 - 9:49
    The other thing we also spontaneously saw,
  • 9:49 - 9:51
    embarrassingly enough,
  • 9:51 - 9:53
    is spontaneous evidence of larceny.
  • 9:53 - 9:56
    The monkeys would rip-off the tokens at every available opportunity --
  • 9:56 - 9:58
    from each other, often from us --
  • 9:58 - 10:00
    you know, things we didn't necessarily think we were introducing,
  • 10:00 - 10:02
    but things we spontaneously saw.
  • 10:02 - 10:04
    So we said, this looks bad.
  • 10:04 - 10:06
    Can we actually see if the monkeys
  • 10:06 - 10:09
    are doing exactly the same dumb things as humans do?
  • 10:09 - 10:11
    One possibility is just kind of let
  • 10:11 - 10:13
    the monkey financial system play out,
  • 10:13 - 10:15
    you know, see if they start calling us for bailouts in a few years.
  • 10:15 - 10:17
    We were a little impatient so we wanted
  • 10:17 - 10:19
    to sort of speed things up a bit.
  • 10:19 - 10:21
    So we said, let's actually give the monkeys
  • 10:21 - 10:23
    the same kinds of problems
  • 10:23 - 10:25
    that humans tend to get wrong
  • 10:25 - 10:27
    in certain kinds of economic challenges,
  • 10:27 - 10:29
    or certain kinds of economic experiments.
  • 10:29 - 10:32
    And so, since the best way to see how people go wrong
  • 10:32 - 10:34
    is to actually do it yourself,
  • 10:34 - 10:36
    I'm going to give you guys a quick experiment
  • 10:36 - 10:38
    to sort of watch your own financial intuitions in action.
  • 10:38 - 10:40
    So imagine that right now
  • 10:40 - 10:42
    I handed each and every one of you
  • 10:42 - 10:45
    a thousand U.S. dollars -- so 10 crisp hundred dollar bills.
  • 10:45 - 10:47
    Take these, put it in your wallet
  • 10:47 - 10:49
    and spend a second thinking about what you're going to do with it.
  • 10:49 - 10:51
    Because it's yours now; you can buy whatever you want.
  • 10:51 - 10:53
    Donate it, take it, and so on.
  • 10:53 - 10:56
    Sounds great, but you get one more choice to earn a little bit more money.
  • 10:56 - 10:59
    And here's your choice: you can either be risky,
  • 10:59 - 11:01
    in which case I'm going to flip one of these monkey tokens.
  • 11:01 - 11:03
    If it comes up heads, you're going to get a thousand dollars more.
  • 11:03 - 11:05
    If it comes up tails, you get nothing.
  • 11:05 - 11:08
    So it's a chance to get more, but it's pretty risky.
  • 11:08 - 11:11
    Your other option is a bit safe. Your just going to get some money for sure.
  • 11:11 - 11:13
    I'm just going to give you 500 bucks.
  • 11:13 - 11:16
    You can stick it in your wallet and use it immediately.
  • 11:16 - 11:18
    So see what your intuition is here.
  • 11:18 - 11:21
    Most people actually go with the play-it-safe option.
  • 11:21 - 11:24
    Most people say, why should I be risky when I can get 1,500 dollars for sure?
  • 11:24 - 11:26
    This seems like a good bet. I'm going to go with that.
  • 11:26 - 11:28
    You might say, eh, that's not really irrational.
  • 11:28 - 11:30
    People are a little risk-averse. So what?
  • 11:30 - 11:32
    Well, the "so what?" comes when start thinking
  • 11:32 - 11:34
    about the same problem
  • 11:34 - 11:36
    set up just a little bit differently.
  • 11:36 - 11:38
    So now imagine that I give each and every one of you
  • 11:38 - 11:41
    2,000 dollars -- 20 crisp hundred dollar bills.
  • 11:41 - 11:43
    Now you can buy double to stuff you were going to get before.
  • 11:43 - 11:45
    Think about how you'd feel sticking it in your wallet.
  • 11:45 - 11:47
    And now imagine that I have you make another choice
  • 11:47 - 11:49
    But this time, it's a little bit worse.
  • 11:49 - 11:52
    Now, you're going to be deciding how you're going to lose money,
  • 11:52 - 11:54
    but you're going to get the same choice.
  • 11:54 - 11:56
    You can either take a risky loss --
  • 11:56 - 11:59
    so I'll flip a coin. If it comes up heads, you're going to actually lose a lot.
  • 11:59 - 12:02
    If it comes up tails, you lose nothing, you're fine, get to keep the whole thing --
  • 12:02 - 12:05
    or you could play it safe, which means you have to reach back into your wallet
  • 12:05 - 12:08
    and give me five of those $100 bills, for certain.
  • 12:08 - 12:11
    And I'm seeing a lot of furrowed brows out there.
  • 12:11 - 12:13
    So maybe you're having the same intuitions
  • 12:13 - 12:15
    as the subjects that were actually tested in this,
  • 12:15 - 12:17
    which is when presented with these options,
  • 12:17 - 12:19
    people don't choose to play it safe.
  • 12:19 - 12:21
    They actually tend to go a little risky.
  • 12:21 - 12:24
    The reason this is irrational is that we've given people in both situations
  • 12:24 - 12:26
    the same choice.
  • 12:26 - 12:29
    It's a 50/50 shot of a thousand or 2,000,
  • 12:29 - 12:31
    or just 1,500 dollars with certainty.
  • 12:31 - 12:34
    But people's intuitions about how much risk to take
  • 12:34 - 12:36
    varies depending on where they started with.
  • 12:36 - 12:38
    So what's going on?
  • 12:38 - 12:40
    Well, it turns out that this seems to be the result
  • 12:40 - 12:43
    of at least two biases that we have at the psychological level.
  • 12:43 - 12:46
    One is that we have a really hard time thinking in absolute terms.
  • 12:46 - 12:48
    You really have to do work to figure out,
  • 12:48 - 12:50
    well, one option's a thousand, 2,000;
  • 12:50 - 12:52
    one is 1,500.
  • 12:52 - 12:55
    Instead, we find it very easy to think in very relative terms
  • 12:55 - 12:58
    as options change from one time to another.
  • 12:58 - 13:01
    So we think of things as, "Oh, I'm going to get more," or "Oh, I'm going to get less."
  • 13:01 - 13:03
    This is all well and good, except that
  • 13:03 - 13:05
    changes in different directions
  • 13:05 - 13:07
    actually effect whether or not we think
  • 13:07 - 13:09
    options are good or not.
  • 13:09 - 13:11
    And this leads to the second bias,
  • 13:11 - 13:13
    which economists have called loss aversion.
  • 13:13 - 13:16
    The idea is that we really hate it when things go into the red.
  • 13:16 - 13:18
    We really hate it when we have to lose out on some money.
  • 13:18 - 13:20
    And this means that sometimes we'll actually
  • 13:20 - 13:22
    switch our preferences to avoid this.
  • 13:22 - 13:24
    What you saw in that last scenario is that
  • 13:24 - 13:26
    subjects get risky
  • 13:26 - 13:29
    because they want the small shot that there won't be any loss.
  • 13:29 - 13:31
    That means when we're in a risk mindset --
  • 13:31 - 13:33
    excuse me, when we're in a loss mindset,
  • 13:33 - 13:35
    we actually become more risky,
  • 13:35 - 13:37
    which can actually be really worrying.
  • 13:37 - 13:40
    These kinds of things play out in lots of bad ways in humans.
  • 13:40 - 13:43
    They're why stock investors hold onto losing stocks longer --
  • 13:43 - 13:45
    because they're evaluating them in relative terms.
  • 13:45 - 13:47
    They're why people in the housing market refused to sell their house --
  • 13:47 - 13:49
    because they don't want to sell at a loss.
  • 13:49 - 13:51
    The question we were interested in
  • 13:51 - 13:53
    is whether the monkeys show the same biases.
  • 13:53 - 13:56
    If we set up those same scenarios in our little monkey market,
  • 13:56 - 13:58
    would they do the same thing as people?
  • 13:58 - 14:00
    And so this is what we did, we gave the monkeys choices
  • 14:00 - 14:03
    between guys who were safe -- they did the same thing every time --
  • 14:03 - 14:05
    or guys who were risky --
  • 14:05 - 14:07
    they did things differently half the time.
  • 14:07 - 14:09
    And then we gave them options that were bonuses --
  • 14:09 - 14:11
    like you guys did in the first scenario --
  • 14:11 - 14:13
    so they actually have a chance more,
  • 14:13 - 14:16
    or pieces where they were experiencing losses --
  • 14:16 - 14:18
    they actually thought they were going to get more than they really got.
  • 14:18 - 14:20
    And so this is what this looks like.
  • 14:20 - 14:22
    We introduced the monkeys to two new monkey salesmen.
  • 14:22 - 14:24
    The guy on the left and right both start with one piece of grape,
  • 14:24 - 14:26
    so it looks pretty good.
  • 14:26 - 14:28
    But they're going to give the monkeys bonuses.
  • 14:28 - 14:30
    The guy on the left is a safe bonus.
  • 14:30 - 14:33
    All the time, he adds one, to give the monkeys two.
  • 14:33 - 14:35
    The guy on the right is actually a risky bonus.
  • 14:35 - 14:38
    Sometimes the monkeys get no bonus -- so this is a bonus of zero.
  • 14:38 - 14:41
    Sometimes the monkeys get two extra.
  • 14:41 - 14:43
    For a big bonus, now they get three.
  • 14:43 - 14:45
    But this is the same choice you guys just faced.
  • 14:45 - 14:48
    Do the monkeys actually want to play it safe
  • 14:48 - 14:50
    and then go with the guy who's going to do the same thing on every trial,
  • 14:50 - 14:52
    or do they want to be risky
  • 14:52 - 14:54
    and try to get a risky, but big, bonus,
  • 14:54 - 14:56
    but risk the possibility of getting no bonus.
  • 14:56 - 14:58
    People here played it safe.
  • 14:58 - 15:00
    Turns out, the monkeys play it safe too.
  • 15:00 - 15:02
    Qualitatively and quantitatively,
  • 15:02 - 15:04
    they choose exactly the same way as people,
  • 15:04 - 15:06
    when tested in the same thing.
  • 15:06 - 15:08
    You might say, well, maybe the monkeys just don't like risk.
  • 15:08 - 15:10
    Maybe we should see how they do with losses.
  • 15:10 - 15:12
    And so we ran a second version of this.
  • 15:12 - 15:14
    Now, the monkeys meet two guys
  • 15:14 - 15:16
    who aren't giving them bonuses;
  • 15:16 - 15:18
    they're actually giving them less than they expect.
  • 15:18 - 15:20
    So they look like they're starting out with a big amount.
  • 15:20 - 15:22
    These are three grapes; the monkey's really psyched for this.
  • 15:22 - 15:25
    But now they learn these guys are going to give them less than they expect.
  • 15:25 - 15:27
    They guy on the left is a safe loss.
  • 15:27 - 15:30
    Every single time, he's going to take one of these away
  • 15:30 - 15:32
    and give the monkeys just two.
  • 15:32 - 15:34
    the guy on the right is the risky loss.
  • 15:34 - 15:37
    Sometimes he gives no loss, so the monkeys are really psyched,
  • 15:37 - 15:39
    but sometimes he actually gives a big loss,
  • 15:39 - 15:41
    taking away two to give the monkeys only one.
  • 15:41 - 15:43
    And so what do the monkeys do?
  • 15:43 - 15:45
    Again, same choice; they can play it safe
  • 15:45 - 15:48
    for always getting two grapes every single time,
  • 15:48 - 15:51
    or they can take a risky bet and choose between one and three.
  • 15:51 - 15:54
    The remarkable thing to us is that, when you give monkeys this choice,
  • 15:54 - 15:56
    they do the same irrational thing that people do.
  • 15:56 - 15:58
    They actually become more risky
  • 15:58 - 16:01
    depending on how the experimenters started.
  • 16:01 - 16:03
    This is crazy because it suggests that the monkeys too
  • 16:03 - 16:05
    are evaluating things in relative terms
  • 16:05 - 16:08
    and actually treating losses differently than they treat gains.
  • 16:08 - 16:10
    So what does all of this mean?
  • 16:10 - 16:12
    Well, what we've shown is that, first of all,
  • 16:12 - 16:14
    we can actually give the monkeys a financial currency,
  • 16:14 - 16:16
    and they do very similar things with it.
  • 16:16 - 16:18
    They do some of the smart things we do,
  • 16:18 - 16:20
    some of the kind of not so nice things we do,
  • 16:20 - 16:22
    like steal it and so on.
  • 16:22 - 16:24
    But they also do some of the irrational things we do.
  • 16:24 - 16:26
    They systematically get things wrong
  • 16:26 - 16:28
    and in the same ways that we do.
  • 16:28 - 16:30
    This is the first take-home message of the Talk,
  • 16:30 - 16:32
    which is that if you saw the beginning of this and you thought,
  • 16:32 - 16:34
    oh, I'm totally going to go home and hire a capuchin monkey financial adviser.
  • 16:34 - 16:36
    They're way cuter than the one at ... you know --
  • 16:36 - 16:38
    Don't do that; they're probably going to be just as dumb
  • 16:38 - 16:41
    as the human one you already have.
  • 16:41 - 16:43
    So, you know, a little bad -- Sorry, sorry, sorry.
  • 16:43 - 16:45
    A little bad for monkey investors.
  • 16:45 - 16:48
    But of course, you know, the reason you're laughing is bad for humans too.
  • 16:48 - 16:51
    Because we've answered the question we started out with.
  • 16:51 - 16:53
    We wanted to know where these kinds of errors came from.
  • 16:53 - 16:55
    And we started with the hope that maybe we can
  • 16:55 - 16:57
    sort of tweak our financial institutions,
  • 16:57 - 17:00
    tweak our technologies to make ourselves better.
  • 17:00 - 17:03
    But what we've learn is that these biases might be a deeper part of us than that.
  • 17:03 - 17:05
    In fact, they might be due to the very nature
  • 17:05 - 17:07
    of our evolutionary history.
  • 17:07 - 17:09
    You know, maybe it's not just humans
  • 17:09 - 17:11
    at the right side of this chain that's duncey.
  • 17:11 - 17:13
    Maybe it's sort of duncey all the way back.
  • 17:13 - 17:16
    And this, if we believe the capuchin monkey results,
  • 17:16 - 17:18
    means that these duncey strategies
  • 17:18 - 17:20
    might be 35 million years old.
  • 17:20 - 17:22
    That's a long time for a strategy
  • 17:22 - 17:25
    to potentially get changed around -- really, really old.
  • 17:25 - 17:27
    What do we know about other old strategies like this?
  • 17:27 - 17:30
    Well, one thing we know is that they tend to be really hard to overcome.
  • 17:30 - 17:32
    You know, think of our evolutionary predilection
  • 17:32 - 17:35
    for eating sweet things, fatty things like cheesecake.
  • 17:35 - 17:37
    You can't just shut that off.
  • 17:37 - 17:40
    You can't just look at the dessert cart as say, "No, no, no. That looks disgusting to me."
  • 17:40 - 17:42
    We're just built differently.
  • 17:42 - 17:44
    We're going to perceive it as a good thing to go after.
  • 17:44 - 17:46
    My guess is that the same thing is going to be true
  • 17:46 - 17:48
    when humans are perceiving
  • 17:48 - 17:50
    different financial decisions.
  • 17:50 - 17:52
    When you're watching your stocks plummet into the red,
  • 17:52 - 17:54
    when you're watching your house price go down,
  • 17:54 - 17:56
    you're not going to be able to see that
  • 17:56 - 17:58
    in anything but old evolutionary terms.
  • 17:58 - 18:00
    This means that the biases
  • 18:00 - 18:02
    that lead investors to do badly,
  • 18:02 - 18:04
    that lead to the foreclosure crisis
  • 18:04 - 18:06
    are going to be really hard to overcome.
  • 18:06 - 18:08
    So that's the bad news. The question is: is there any good news?
  • 18:08 - 18:10
    I'm supposed to be up here telling you the good news.
  • 18:10 - 18:12
    Well, the good news, I think,
  • 18:12 - 18:14
    is what I started with at the beginning of the Talk,
  • 18:14 - 18:16
    which is that humans are not only smart;
  • 18:16 - 18:18
    we're really inspirationally smart
  • 18:18 - 18:21
    to the rest of the animals in the biological kingdom.
  • 18:21 - 18:24
    We're so good at overcoming our biological limitations --
  • 18:24 - 18:26
    you know, I flew over here in an airplane.
  • 18:26 - 18:28
    I didn't have to try to flap my wings.
  • 18:28 - 18:31
    I'm wearing contact lenses now so that I can see all of you.
  • 18:31 - 18:34
    I don't have to rely on my own near-sightedness.
  • 18:34 - 18:36
    We actually have all of these cases
  • 18:36 - 18:39
    where we overcome our biological limitations
  • 18:39 - 18:42
    through technology and other means, seemingly pretty easily.
  • 18:42 - 18:45
    But we have to recognize that we have those limitations.
  • 18:45 - 18:47
    And here's the rub.
  • 18:47 - 18:49
    It was Camus who once said that, "Man is the only species
  • 18:49 - 18:52
    who refuses to be what he really is."
  • 18:52 - 18:54
    But the irony is that
  • 18:54 - 18:56
    it might only be in recognizing our limitations
  • 18:56 - 18:58
    that we can really actually overcome them.
  • 18:58 - 19:01
    The hope is that you all will think about your limitations,
  • 19:01 - 19:04
    not necessarily as unovercomable,
  • 19:04 - 19:06
    but to recognize them, accept them
  • 19:06 - 19:09
    and then use the world of design to actually figure them out.
  • 19:09 - 19:12
    That might be the only way that we will really be able
  • 19:12 - 19:14
    to achieve our own human potential
  • 19:14 - 19:17
    and really be the noble species we hope to all be.
  • 19:17 - 19:19
    Thank you.
  • 19:19 - 19:24
    (Applause)
Title:
A monkey economy as irrational as ours
Speaker:
Laurie Santos
Description:

Laurie Santos looks for the roots of human irrationality by watching the way our primate relatives make decisions. A clever series of experiments in "monkeynomics" shows that some of the silly choices we make, monkeys make too.

more » « less
Video Language:
English
Team:
closed TED
Project:
TEDTalks
Duration:
19:25
TED edited English subtitles for A monkey economy as irrational as ours
TED added a translation

English subtitles

Revisions Compare revisions